Generative AI: what it is, how it works, types and examples

Generative AI allows you to create text, images, video, audio, code and other content from an instruction, a reference image, a document or different types of data.

These systems do not function as a database that simply copies a stored response. They use trained models to represent patterns and generate a probable output based on the received context.

However, just because content is new does not automatically mean that it is correct, legally original, or suitable for publication without review.

In this guide you will learn what generative AI is, how it works, what types of models exist, what it can create, and what controls you should apply to use it responsibly.

To learn in advance about the main branches, applications, advantages and risks of AI, you can consult our complete guide to artificial intelligence.

What is generative artificial intelligence

Generative artificial intelligence is a category of AI designed to produce new outputs from patterns learned during training.

It can generate:

• Text.
• Images.
• Video.
• Voice.
• Music.
• Code.
• Designs.
• Presentations.
• Synthetic data.
• Three-dimensional models.

The result is constructed from input provided by the user or another system.

That entry could be:

• A written instruction.
• A question.
• A photograph.
• A sketch.
• A document.
• An audio file.
• A video sequence.
• Structured data.
• A combination of several formats.

The output is synthetic because it has been produced by the model.

It should not be assumed to be true, unpublished, legally original, or suitable for any use.

Differences between predictive, discriminative and generative AI

Systems can be classified according to the type of result they produce.

A discriminative model could calculate whether an image contains a dog or a cat.

A generative model could produce a new image from the instruction:

Create an illustration of a dog and a cat traveling by train.

In a real application, both capabilities can be combined. A system can analyze an image, retrieve information, classify it, and subsequently generate an explanation.

Therefore, “predictive” and “generative” do not always represent completely separate products, but rather capabilities that can be part of the same service.

How generative AI works step by step

The exact process depends on the model, but can be summarized in five phases.

Model training

During training, the model analyzes large data sets to learn statistical relationships and representations.

Depending on the format, you can learn:

• Language structures.
• Relationships between words.
• Forms.
• Colors.
• Textures.
• Sounds.
• Motion.
• Code patterns.
• Relationships between text and image.

The model does not necessarily store each example as a file that it will later retrieve.

It adjusts numerous internal parameters that allow it to represent patterns and produce results.

The origin, quality, diversity and permissions associated with the data influence the final behavior.

The full explanation of data, training, models, and inference can be found in our guide on how artificial intelligence works.

Prompt and input data

The prompt is the instruction or information that the system receives.

You can indicate:

• The task.
• The context.
• The format.
• The public.
• The style.
• The restrictions.
• The data you should use.
• The elements you should avoid.

Imprecise example:

Write a text about marketing.

Most useful example:

Write a 150-word introduction for small online store owners. Explain how AI can help organize customer inquiries without promising increased sales.

The second instruction reduces ambiguity and allows for a more tailored result.

Context and representation

The application transforms the input into a representation that the model can process.

In a language model, text is usually divided into units called tokens.

A token can represent:

• One word.
• Part of a word.
• A sign.
• A symbol.
• Part of a code.

The model analyzes the tokens and the available context to estimate which exit is most likely.

Images, audio, video or documents can also be processed in multimodal systems.

Probabilistic generation

The model produces the result progressively.

In a language model, it calculates probabilities on possible subsequent tokens and selects a sequence based on its configuration.

In an image model, the process can start with noise and gradually transform it until an image consistent with the instruction is obtained.

Probabilistic generation explains why:

• The same instruction can produce different results.
• The system can offer several versions.
• An answer may seem convincing and be incorrect.
• Small changes to the prompt can greatly alter the output.

Result and review

The generated output must be evaluated before use.

Check:

• Accuracy.
• Coherence.
• Sources.
• Spelling.
• Personal data.
• Possible biases.
• Problematic similarities.
• Suitable for the public.
• Rights of use.
• Fulfillment of the purpose.

Generation does not represent the end of the process.

In professional jobs, the correct flow is usually:

Objective → Prompt → Generation → Review → Correction → Validation → Publication

Main types of generative models

There is no single architecture to generate all formats.

Language models and transformers

Language models are trained to work with sequences of text or code.

Large language models, known as LLMs, can be used to:

• Write.
• Summarize.
• Translate.
• Answer questions.
• Extract information.
• Generate code.
• Reorganize documents.
• Hold conversations.

Many modern models use transformer architectures, capable of processing relationships within the context through attention mechanisms.

The model estimates which tokens can continue the sequence.

This does not mean that I simply copy an existing phrase. Generates an output based on the relationships represented in its parameters and the received context.

However, predicting a probable sequence does not guarantee that the content is true either.

Diffusion models

Diffusion models are especially used to generate and edit images and video.

During training they learn to reverse a process of noise degradation or addition.

In simplified terms:

1. It starts from a representation with noise.
2. The model progressively eliminates noise.
3. The instruction conditions the process.
4. The image acquires structure, shapes and details.
5. The final result is produced.

They can also use:

• A reference image.
• A mask.
• A sketch.
• A pose.
• A depth map.
• A previous composition.

This allows you to perform tasks such as:

• Text to image.
• Image by image.
• Filling of areas.
• Expansion.
• Change of style.
• Localized edition.
• Video generation.

Generative adversarial networks

GANs or generative adversarial networks use two components:

• A generator.
• A discriminator.

The generator attempts to produce samples similar to the actual data.

The discriminator attempts to distinguish the actual samples from the generated ones.

During training, both improve through this competition.

GANs have been used to create images, synthetic faces, data, and visual transformations. They currently coexist with diffusion models and other generative architectures.

Multimodal models

A multimodal model can process or generate various formats.

For example:

• Receive text and image.
• Interpret a document.
• Reply on a graph.
• Generate an image from text.
• Analyze audio and produce a summary.
• Combine video, voice and language.

Multimodality does not mean that the system understands all formats with the same precision.

Each modality may have different capabilities and limitations.

Foundational models

A foundational model is a large-scale model trained on broad and reusable information for numerous tasks.

It can serve as a basis for:

• Assistants.
• Generators.
• Search engines.
• Agents.
• Business tools.
• Specialized applications.

It can then be adapted by:

• Instructions.
• Fine adjustment.
• Specific data.
• Document recovery.
• Tools.
• Rules and filters.

Not all generative AI applications train their own model from scratch. Many integrate a foundational model within a broader product.

What content can generative AI create

Text generation

It can help produce:

• Drafts.
• Emails.
• Summaries.
• Scripts.
• Descriptions.
• Initial reports.
• Questions.
• Schemes.
• Translations.
• Variations of a message.

The content needs revision because the model can:

• Invent data.
• Omit information.
• Repeat.
• Use a generic tone.
• Confuse dates.
• Produce non-existent quotes.

To compare text assistants you can consult our guide on ChatGPT, Gemini and Claude.

Image generation

It can generate:

• Illustrations.
• Synthetic photography.
• Conceptual designs.
• Advertising images.
• Product variations.
• Funds.
• Textures.
• Initial infographics.
• Sketches.
• Characters.

Quality depends on:

• Model.
• Resolution.
• Prompt.
• References.
• Composition.
• Later edition.
• Anatomical and spatial coherence.

Our guide on How to create images with artificial intelligence Explain the tools and the practical process.

Video generation

Video models can produce:

• Clips from text.
• Animations.
• Movement from images.
• Advertising scenes.
• Transitions.
• Avatars.
• Video with synthetic voice.
• Variations of a sequence.

Among its main difficulties are:

• Coherence between frames.
• Physics.
• Motion.
• Character continuity.
• Text within the scene.
• Exact camera control.
• Duration and cost.

Audio, voice and music generation

It can be used for:

• Narratives.
• Synthetic voices.
• Dubbing.
• Voice translation.
• Effects.
• Music.
• Audio cleaning or reconstruction.
• Sound prototypes.

The use of real voices requires special caution.

Cloning a voice without consent can lead to privacy issues, impersonation, rights, and fraud.

Code generation

Models can:

• Propose functions.
• Create tests.
• Explain code.
• Generate documentation.
• Translate between languages.
• Suggest refactorings.
• Find possible errors.
• Create prototypes.

The generated code must be reviewed and tested.

May contain:

• Vulnerabilities.
• Non-existent dependencies.
• Outdated functions.
• Logical errors.
• Performance issues.
• Incompatible fragments.

Three-dimensional designs and models

Generative AI can also help in:

• Industrial design.
• Conceptual architecture.
• Fashion.
• Three-dimensional modeling.
• Interface design.
• Shape optimization.
• Prototyping.
• Creating assets for video games.

It typically functions as design support and requires technical adaptation before manufacturing or implementing the result.

What is a prompt and why it influences the result

A prompt is the input that guides generation.

May include:

• Instruction.
• Context.
• Examples.
• Format.
• Restrictions.
• Data.
• Role.
• Evaluation criteria.

A useful structure is:

Objective:
What you should do. Context:
Information needed. Audience:
Who will use the result. Format:
How you should respond. Restrictions:
What you should avoid. Criteria:
How it will be evaluated.

The prompt improves the result, but cannot guarantee accuracy.

An excellent instruction does not correct:

• Inadequate training data.
• Lack of access to information.
• Model limitations.
• Errors in the sources.
• A task beyond his capacity.

The guide on how to write better prompts It contains practical examples for different tasks.

Difference between generating, retrieving and editing content

A modern application can combine several functions.

Generate

The model produces a new output.

Example:

Create a product description.

Recover

The system searches for information within documents, databases or the Internet.

Example:

Find the specifications of a product in a documentary database.

Edit

The system transforms existing content.

Example:

Remove the background from a photograph or summarize a document.

Use tools

The system calls an external function.

Example:

Query a database, perform a calculation, or create an event.

Therefore, not everything an AI application does comes exclusively from the generative model.

It can combine generation, search, traditional software, tools, and human review.

Practical applications of generative AI

It can be used as support in:

• Ideation.
• Creating drafts.
• Conceptual design.
• Customer service.
• Training.
• Marketing.
• Programming.
• Initial investigation.
• Prototyping.
• Documentation.
• Personalization.
• Automation.

Its usefulness increases when:

• The task allows for review.
• There is a responsible person.
• The result can be verified.
• Sensitive information is protected.
• The model is used within a clear process.
• The real value is measured.

To learn about use cases by sector, consult our guide on the applications of artificial intelligence.

Advantages of generative AI

Speed to create first versions

It can generate quickly:

• Ideas.
• Schemes.
• Drafts.
• Variations.
• Prototypes.

This does not mean that the end result is immediate. The time saved should be compared to the review work.

Exploring alternatives

It allows you to try different ones:

• Tones.
• Compositions.
• Designs.
• Approaches.
• Scenarios.
• Solutions.

Accessibility

Conversational interfaces allow certain capabilities to be used without the need for programming.

Personalization

You can adapt an output according to:

• Public.
• Level.
• language.
• Format.
• Context.
• Need.

Creative and technical support

It can help you get past a blank page, organize ideas, or explore an initial solution.

It does not automatically replace judgment, experience or professional responsibility.

Risks and limitations of generative AI

Hallucinations and mistakes

Models can generate:

• False data.
• Non-existent sources.
• Incorrect dates.
• Incorrect calculations.
• Made-up quotes.
• Convincing but wrong explanations.

All important factual content must be verified.

Biases

The learned patterns can reproduce:

• Stereotypes.
• Imbalances.
• Omissions.
• Differences in representation.
• Unfair associations.

The review should assess not only formal quality, but also who may be affected.

Privacy and confidential data

You should not enter sensitive information without checking:

• Privacy policy.
• Data usage.
• Retention.
• Service location.
• Training settings.
• Third party access.
• Business conditions.

Examples of sensitive information:

• Personal data.
• Contracts.
• Medical information.
• Passwords.
• Strategies.
• Private code.
• Financial data.
• Customer information.

Copyright and origin

Before using a result, you should consider:

• Platform conditions.
• Source of data.
• Licenses.
• Possible similarities.
• Brands.
• Image rights.
• Voice rights.
• Commercial use.
• Attribution requirements.

Just because a platform allows content to be downloaded does not mean that it guarantees its use in any context.

Deepfakes and misinformation

Image, video and voice generation can be used to:

• Impersonating people.
• Manufacture tests.
• Manipulate statements.
• Create fake news.
• Deceiving customers.
• To commit fraud.

Synthetic content should be identified when its nature may be misleading.

Security and prompt injection

An application connected to documents or tools may receive malicious instructions.

A prompt injection attack attempts to cause the system to ignore its rules, reveal information, or take improper actions.

Controls may include:

• Minimum permits.
• Data separation.
• Validation of actions.
• Filtered.
• Human confirmation.
• Records.
• Limits.
• Safety tests.

Dependency and loss of judgment

Using AI without reviewing can cause:

• Loss of knowledge.
• Uniform content.
• Repeated errors.
• Lower critical capacity.
• Dependence on a platform.
• Lack of responsibility.

AI should support the process, not eliminate understanding of the task.

How to use generative AI responsibly

Before generating:

1. Define the purpose.
2. Check if you can use the data.
3. Avoid entering confidential information.
4. Choose a suitable tool.
5. It establishes what needs human review.

After generating:

1. Verify data and sources.
2. Check calculations.
3. Check biases.
4. Evaluate possible similarities.
5. Correct the tone.
6. Confirm rights and permissions.
7. Identify synthetic content where applicable.
8. He retains editorial responsibility.

For repetitive tasks you can establish a flow:

Authorized entry
→ Generation
→ Automatic validation
→ Human review
→ Approval
→ Publication
→ Follow-up

Transparency of AI-generated content

Transparency is especially important when the content can be mistaken for an actual human recording, statement, or creation.

In the European Union, the Artificial Intelligence Regulation establishes transparency obligations for certain synthetic content.

Among other measures, it requires that providers of systems that generate audio, image, video or synthetic text allow it to be detected that the content has been artificially generated or manipulated.

It also establishes obligations for certain deepfakes and texts generated for the purpose of informing the public about matters of general interest, with specific exceptions and conditions.

These general obligations of Article 50 begin to apply on August 2, 2026.

Transparency should not be limited to complying with a standard.

It also helps to:

• Maintain trust.
• Avoid deception.
• Attribute responsibility.
• Explain the process.
• Differentiate creation, editing, and generation.
• Facilitate verification.

Conclusion

Generative AI produces new content from learned patterns and the context provided.

Language models generate sequences of text or code. Diffusion models produce and edit images or video. GANs use a generator and a discriminator, while multimodal models combine different formats.

The quality of the result depends on the model, data, prompt, context, and review process.

Therefore, using generative AI correctly requires:

• Clear instructions.
• Authorized data.
• Verification.
• Privacy protection.
• Security control.
• Rights review.
• Transparency.
• Human responsibility.

Frequently asked questions about generative AI